Skip to main content
Download PDF

A comparative analysis of drug-induced kidney injury adverse reactions between cyclosporine and tacrolimus based on the FAERS database

BMC Immunology volume 26, Article number: 35 (2025) Cite this article

Abstract

Background

This study utilizes the FDA Adverse Event Reporting System (FAERS) database to compare the adverse reaction signals of cyclosporine and tacrolimus, two widely used immunosuppressants, in relation to drug-induced kidney injury. The findings aim to inform clinical decision-making.

Methods

The study retrospectively analyzed data from January 2004 to September 2024, employing both frequency analysis and Bayesian methods. We assessed and compared the mortality rates, hospitalization rates, and the association of cyclosporine and tacrolimus with kidney injury to elucidate the renal toxicity of these two drugs.

Results

After data processing, we identified a total of 3,449 cyclosporine-related kidney injury reports and 5,538 tacrolimus-related kidney injury reports. The results revealed a stronger association between tacrolimus and kidney injury. Additionally, kidney injuries associated with both cyclosporine and tacrolimus predominantly affected males. Furthermore, the hospitalization rate for cyclosporine-related kidney injury was 34.40%, compared to 44.50% for tacrolimus. The mortality rate associated with cyclosporine-induced kidney injury was higher than that of tacrolimus.

Conclusion

This study utilized the FDA Adverse Event Reporting System (FAERS) database from January 2004 to September 2024 to perform a comprehensive analysis of adverse drug-related kidney injury reactions to cyclosporine and tacrolimus. The results suggest that both cyclosporine and tacrolimus are associated with renal injury, but tacrolimus appears to reduce mortality while increasing hospitalization rates. This serves as a critical warning for planning future treatment regimens, drug monitoring, and reducing adverse effects.

Peer Review reports

Introduction

Nephrotoxicity is the inability of the kidneys to properly detoxify and excrete drugs and toxic chemicals due to destruction or injury caused by endogenous or exogenous toxicants [1]. It is characterized by elevated serum creatinine and urea, decreased GFR (glomerular filtration) rate, and may be accompanied by arterial hypertension. Histologically, there are changes in renal pathology, such as tubular cell swelling, necrosis, arteriolar changes and interstitial fibrosis [2]. Nephrotoxicity is usually caused by a variety of drugs and chemicals or environmental pollutants. Drug-induced nephrotoxicity is about 25% and can be as high as 66% in the elderly [3]. Cyclosporine A (CsA) and tacrolimus (TAC), widely used immunosuppressive agents, play an important role in organ transplantation and treatment of autoimmune diseases. However, both drugs may cause kidney damage. Cyclosporine is a cyclic peptide consisting of 11 amino acids purified from the fungus Topocladium inflatum. CsA is a potent immunosuppressant commonly used to inhibit transplant organ rejection [4]. In solid organ transplantation, CsA significantly improves long-term survival [5]. CsA has a narrow therapeutic index and its metabolism is carried out by hepatic cytochromes (CYP450 3 A 4/5). However, nephrotoxicity is one of the serious adverse effects that limit the therapeutic use of CsA. Several reports have discussed the mechanisms by which CsA induces nephrotoxicity [6]. In 2017, it was shown that CsA mediates renal injury through a variety of mechanisms such as inflammation generation, oxidative stress (OS), autophagy and apoptosis [7]. Tacrolimus (Tac or FK506), a calmodulin phosphatase inhibitor (CNI), is a first-line immunosuppressant, and TAC was first approved by the U.S. Food and Drug Administration for liver transplantation in 1994. Due to its excellent efficacy, Tac has expanded to become a first-line regimen for renal, cardiac, lung, intestinal, and bone marrow transplantation. CNI-induced nephrotoxicity may be related to the intrarenal concentration of CNI, and its metabolites may contribute to its nephrotoxicity [8]. The mechanism of nephrotoxicity of cyclosporine and tacrolimus is complex and involves multiple aspects such as intracellular signaling, oxidative stress, immune response and drug metabolism; in addition, factors such as drug dose, individual patient differences, and coadministration of medications may influence the onset and progression of renal injury [9,10,11,12,13]. However, clinical trial data are usually characterized by strict inclusion criteria and a limited pool of participants, which may not accurately reflect the complexity of real-world clinical scenarios. Therefore, in-depth study and evaluation of the nephrotoxicity of these two drugs is important for optimizing treatment regimens, improving therapeutic efficacy, and reducing adverse effects. The U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database is a database used to support the FDA’s post-market safety monitoring of approved drugs and therapeutic biologics [14]. It contains more than 20.22 million ADE reports. By analyzing the U.S. FAERS database, some potential drug safety issues can be identified and timely measures can be taken to address these issues and protect public health. The aim of this study was to compare the signals of adverse reaction reports of cyclosporine and tacrolimus regarding drug-induced renal injury by mining the U.S. FAERS database, which can provide a reference for the use of clinical drugs.

Methods

Data source and processing

A retrospective pharmacovigilance study was conducted using data retrieved from the January 2004 and September 2024 FAERS databases. A total of 42,392 reports of cyclosporine-related adverse drug events and 56,522 reports of tacrolimus-related adverse drug events were retrieved from the FAERS database. The PRIMARY_ID, CASE_ID, and FDA_DT fields of the DEMO table were selected according to the FDA-recommended de-duplication uploading method and sorted in the order of CASE_ID, FDA_DT, and PRIMARY_ID. For reports with the same CASE_ID, the report with the largest FDA_DT value was kept. For statements with the same CASE_ID and FDA_DT, the statement with the largest PRIMARY_ID value was retained. Ultimately, 3449 reports of cyclosporine-associated kidney injury (4553 adverse reaction signals) and 5538 reports of tacrolimus-associated kidney injury (6370 adverse reaction signals) were identified, with specific related listed in Supplementary Table1.

Data processing and analysis

Disproportionality analysis is a data mining algorithm used to quantitatively detect ADR signals in large pharmacovigilance databases. Disproportionality analysis can be used to evaluate differences between the occurrence frequency and background frequency of the target drug and the target ADE, thereby establishing a statistical association between the drug and the ADE. Commonly used algorithms include frequency counting and Bayesian methods. Frequency count method includes reporting odd ratio (ROR), proportional reporting ratio (PRR), etc.The Bayesian method includes multi-item Gamma possion shrinke (MGPS), Bayesian confidence propagation neural network (BCPNN) and so on [15].

Four methods for ADE signal mining were used in this study, including the ratio of reports (ROR) method, the Bayesian confidence propagation neural network (BCPNN) method, and the multi-item Gamma Poisson Shrinker (MGPS) method. The ROR method originated from the Lareb Laboratory at the Dutch Pharmacovigilance Center, and is characterized by low bias and high sensitivity. The ROR method helps to reduce bias in the underreported events. ROR helps to reduce bias in underreported events. Compared to ROR, PRR stands out due to its higher specificity. Currently, the BCPNN method is a well-established signaling technique that is used both domestically and internationally. It is capable of early signal detection even with low or missing data, and its results become more stable as the number of reports increases, but the method is computationally complex and lacks transparency. In addition, the MGPS method has the advantage of detecting rare event signals. Although there is no gold standard for signal detection methods, each method has its own characteristics and has its own advantages and disadvantages in terms of applicability and feasibility in databases. Threshold conditions for the various methods were as follows lower 95% CI > 1, PRR ≥ 2, χ2 ≥ 4, IC025 > 0, and EBGM05 ≥ 2. In this study, a positive signal for drug-associated AE was considered when at least one of the four algorithms met the criteria; when all four algorithms met the criteria, it indicated that the AE was a more strongly correlated signal, thus avoiding potential false-positive signals. Table 1 shows the calculation formulas and thresholds. The larger the ROR and BCPNN, the stronger the signal, indicating that the target drug is more relevant to the ADE [16, 17].

Table 1 Detection methods, formulas, and thresholds
Full size table

Furthermore, descriptive analysis was employed to summarize the clinical characteristics of kidney injury patients who used cyclosporine and tacrolimus, based on the FAERS database. The mortality and hospitalization rates for cyclosporine and tacrolimus were compared using Pearson’s chi-square test or Fisher’s exact test. Statistical significance was set at p < 0.05, and data analysis was performed using SPSS 15.0.5.

Adverse events and drug identification

“We utilized the MedDRA (Version 26.1) preferred terms to search for adverse reactions associated with kidney injury. The commonly reported kidney injury-related adverse reactions include: acute kidney injury (10069339), kidney dysfunction (10062237), abnormal serum creatinine (10005481), elevated serum creatinine (10005483), abnormal serum urea (10005846), elevated serum urea (10005851), abnormal glomerular filtration rate (10018356), decreased glomerular filtration rate (10018358), oliguria (10030302), peritoneal dialysis (10034660), dialysis (10061105), continuous hemodialysis filtration (10066338), proteinuria (10037032), toxic nephropathy (10029155), renal tubular dysfunction (10050335), tubular necrosis (10038540), allergic nephritis (10029120), and tubulointerstitial nephritis (10048302). Focusing on cyclosporine and tacrolimus as the drugs of interest, we performed a compound search using both the generic names (“Cyclosporine”, “Tacrolimus”) and brand names (“Sandimmun”, “FK506”). The drugs were identified as the primary suspected agents, leading to the collection of adverse drug event (ADE) reports where cyclosporine and tacrolimus were the primary suspects.”

Result

Signal detection for cyclosporine and tacrolimus-related kidney injury

From January 2004 to September 2024, a total of 42,392 cyclosporine-related adverse drug event reports and 56,522 tacrolimus-related adverse drug event reports were retrieved from the FAERS database. Duplicate records were removed in accordance with FDA guidelines. In the end, 4,553 adverse reaction signals related to cyclosporine-induced kidney injury and 6,370 signals related to tacrolimus-induced kidney injury were identified. Table 2 presents the kidney injury signals for cyclosporine and tacrolimus based on the criteria from four different algorithms. A comparison of the association between cyclosporine and tacrolimus with kidney injury reveals that tacrolimus shows a stronger correlation with kidney injury (ROR = 5.17, PRR = 5.02, EBGM05 = 4.95, IC025 = 2.31).

Table 2 Signal detection of kidney injury related to cyclosporine and tacrolimus
Full size table

Baseline information

As seen in Table 3, there were 3449 reports of cyclosporine-associated kidney injury and 5538 reports of tacrolimus-associated kidney injury. The majority of these injuries occurred in males, with health practitioners being the primary reporters; analysis of the age distribution in the known population showed that AEs were concentrated in the 48–68 year age range; analysis of the weight distribution in the known population showed that AEs were concentrated in the 50–100 kg weight range; the top 5 countries for cyclosporine reports were Japan, the United States, Canada, France, and Germany; the number of reported cases of cyclosporine was relatively uniform from 2004 to 2024, remaining above 100 cases per year, whereas the number of reported cases of tacrolimus showed a gradual increase from 2004 to 2024 and reached the maximum in 2018, and then remained above 400 cases; the specific basic characteristics are shown in Tables 3 and 4, and Fig. 1. Table 4; Fig. 2. To ensure the data quality and reporting bias, we further compared the results of the four algorithms of all-positive renal impairment of the two drugs, and the details are shown in Table 5. We can see that the adverse reactions associated with renal impairment of all-positive renal impairment of the two drugs in the four algorithms are Renal impairment, Blood creatinine increased, Oliguria, Peritoneal dialysis, Dialysis, Continuous haemodiafiltration, Proteinuria, Nephropathy toxic, Renal tubular necrosis and Tubulointerstitial nephritis, suggesting a stronger association between the two drugs and the aforementioned kidney injury-related adverse effects.

Table 3 Basic information of adverse event reports related to kidney injury from cyclosporine and tacrolimus
Full size table
Table 4 Top 5 countries by number of adverse event reports related to kidney injury from cyclosporine and tacrolimus
Full size table
Fig. 1
figure 1

Number of Adverse Event Reports Related to Kidney Injury from Cyclosporine and Tacrolimus from Q1 2004 to Q3 2024

Full size image
Table 5 All-positive for four algorithms for cyclosporine and tacrolimus renal impairment
Full size table

Mortality and hospitalization due to kidney dysfunction caused by cyclosporine and tacrolimus

To evaluate the prognosis of kidney injury caused by cyclosporine and tacrolimus, we analyzed the proportions of patients who died or were hospitalized due to kidney dysfunction. The hospitalization rate for cyclosporine-related kidney injury was 34.40%, compared to 44.50% for tacrolimus-related kidney injury. However, the mortality rate for cyclosporine-induced kidney injury was slightly higher than that for tacrolimus (17.10% versus 12.70%), and the details are shown in Table 6. Significant differences were observed in both hospitalization and mortality rates between cyclosporine and tacrolimus (Chi-square test, p < 0.05).

Table 6 Mortality and hospitalization rates due to kidney dysfunction caused by cyclosporine and tacrolimus
Full size table

Detection results of adverse event risk signals related to kidney dysfunction caused by cyclosporine and tacrolimus

The association between drugs and kidney injury is classified based on the ROR values. For cyclosporine-related kidney injury, the highest association is with toxic nephropathy (ROR = 22.6); strong associations are found with increased serum creatinine (ROR = 6.47), oliguria (ROR = 6.48), peritoneal dialysis (ROR = 7.47), and proteinuria (ROR = 7.35); significant associations include kidney function impairment (ROR = 5.14), abnormal serum creatinine (ROR = 2.54), elevated blood urea (ROR = 4.91), decreased glomerular filtration rate (ROR = 2.63), dialysis (ROR = 2.5), continuous hemodiafiltration (ROR = 4.39), tubular necrosis (ROR = 4.61), and tubulointerstitial nephritis (ROR = 2.56); moderate association is with acute kidney injury (ROR = 1.29); and non-significant associations are with abnormal blood urea (ROR = 1.29), abnormal glomerular filtration rate (ROR = 1.44), and allergic nephritis (ROR = 1).For tacrolimus-related kidney injury, the highest association is with toxic nephropathy (ROR = 18.98), continuous hemodiafiltration (ROR = 8.97), tubular dysfunction (ROR = 8.88), and tubular necrosis (ROR = 10.92); strong associations are found with abnormal serum creatinine (ROR = 6.44) and dialysis (ROR = 3.73); significant associations include acute kidney injury (ROR = 4.15), kidney function impairment (ROR = 5.22), increased serum creatinine (ROR = 5.75), abnormal glomerular filtration rate (ROR = 3.75), decreased glomerular filtration rate (ROR = 2.05), oliguria (ROR = 4.45), proteinuria (ROR = 5.43), and tubulointerstitial nephritis (ROR = 3.63); moderate association is with elevated blood urea (ROR = 2.26); and non-significant associations are with abnormal blood urea (ROR = 1.86) and allergic nephritis (ROR = 1.73). See Fig. 2 for details. The adverse event of kidney injury most strongly associated with both cyclosporine and tacrolimus is toxic nephropathy. The most reported adverse reaction related to kidney injury with cyclosporine is increased serum creatinine, while with tacrolimus, it is acute kidney function impairment. See Fig. 3.

Fig. 2
figure 2

Comparison of adverse events related to kidney injury between cyclosporine and tacrolimus

Full size image
Fig. 3
figure 3

Cyclosporine and Tacrolimus-Related Kidney Injury

Full size image

  • ROR (Reporting Odds Ratio): The size of the bubbles represents the number of reports, with larger bubbles indicating more reports. The color intensity indicates the ROR value, with darker colors representing higher ROR values.

Onset time of kidney injury associated with cyclosporine and tacrolimus

In Fig. 4, we describe the onset time of kidney injury adverse events related to cyclosporine and tacrolimus. The onset of adverse events related to cyclosporine and tacrolimus-induced kidney injury primarily occurs within 0–240 days. The median onset time for cyclosporine-related kidney injury is 70 days, while for tacrolimus-related kidney injury, it is 80 days. Additionally, the average onset time for cyclosporine-related kidney injury is approximately 587 days, and for tacrolimus-related kidney injury, it is approximately 524 day.

Fig. 4
figure 4

Onset time of kidney injury associated with cyclosporine and tacrolimus

Full size image

Discussion

This study represents the largest-scale investigation to date, examining the differences in susceptible populations, onset times, and adverse outcomes of kidney injury associated with cyclosporine and tacrolimus in real-world settings, based on the FAERS database from January 2004 to September 2024. Previous studies either had smaller sample sizes [18,19,20], or focused solely on populations with specific disease backgrounds or surgical procedures [21, 22], limiting their ability to provide a comprehensive view of kidney injury caused by these two drugs. Furthermore, they did not directly compare the incidence of kidney injury between cyclosporine and tacrolimus, particularly regarding varying onset times.

Our study showed that cyclosporine and tacrolimus were associated with renal injury, with tacrolimus showing a more pronounced association than cyclosporine (ROR = 5.17). Among the number of reported kidney injuries, 4553 and 6370 occurred for cyclosporine and tacrolimus, respectively. It is clear from the baseline information that kidney injury associated with both drugs mainly affects males, which may be related to the fact that males receive higher doses of the drugs due to their body weight or doctor’s prescribing habits; in some autoimmune diseases, rheumatoid arthritis is more prevalent than males, although the overall prevalence is higher in women than in men [23, 24]. Some studies have shown that male rheumatoid arthritis patients in the active phase of the disease, the use of cyclosporine or tacrolimus to control the disease, due to the characteristics of their autoimmune system, the inflammatory response in the body may be more intense, in the process of receiving immunosuppressive therapy, the kidneys are more likely to be subject to drug toxicity and the disease itself, the double blow of immune damage [25]. In the field of organ transplantation, taking kidney transplantation as an example, male kidney transplant recipients have greater fluctuations in drug concentration in their bodies after the use of tacrolimus, and are more likely to go beyond the therapeutic window, thus increasing the risk of kidney injury [26]. From the perspective of genetic factors, males and females differ in the expression of certain genes, and the distribution of CYP3 A5 gene polymorphisms, which are closely related to the metabolism of cyclosporine and tacrolimus, differs between males and females [9, 27]. Male patients carrying specific CYP3 A5 genotypes may be at increased risk of renal injury due to slow drug metabolism, leading to drug accumulation in the body [28,29,30]. Adverse events related to kidney injury caused by both drugs were mainly reported by health practitioners (> 80%), which adds to the credibility of this study. In addition, to assess the prognosis associated with renal injury with both drugs, we listed the percentage of patients who were hospitalized and died due to renal injury associated with both drugs; the hospitalization rate for cyclosporine-associated renal injury was 34.40%, and for tacrolimus-associated renal injury was 44.50%, but deaths caused by cyclosporine-associated renal injury were slightly higher than those caused by tacrolimus (17.10% vs. 12.70%), and there was a significant difference between cyclosporine and tacrolimus in both hospitalization and mortality rates.), and there was a significant difference in both hospitalization and mortality rates between cyclosporine and tacrolimus (chi-square test, p < 0.05). The superior efficacy of TAC in preventing graft rejection reduces mortality, but surviving patients may experience long-term renal impairment requiring hospitalization. It has been shown that TAC-treated patients have a 30% reduction in graft loss but a 25% increase in hospitalization related to acute kidney injury compared to CsA [31, 32]. Also in terms of delayed toxicity recognition, this study showed that the median onset of TAC-induced nephrotoxicity (e.g., tubular necrosis) was 80 days by retrospective cohort analysis, which was significantly later than 70 days for CsA (P < 0.05). This delay is associated with the mechanism of interstitial fibrosis mediated by TAC through TGF-β1, leading to insidious symptoms in the early stages of injury, which have progressed to more severe stages by the time of diagnosis (e.g., tubular necrosis ROR = 10.92) [33, 34]. This is consistent with our data showing that TAC users have a higher risk ratio (ROR = 10.92) for serious events such as tubular necrosis. In clinical practice, the widespread use of TACs has increased the absolute number of exposed patients and increased hospitalization rates, as shown in the survey study. In addition, clinicians may hospitalize TAC patients more frequently because of protocol-driven surveillance. Together, these factors explain the discrepancy between mortality and hospitalization rates, emphasizing the importance of clinical drug monitoring and early renal function monitoring. Therefore, caution must be exercised in the selection of both inhibitors to prevent hospitalization and death due to drug-induced renal injury.

Renal injury induced by cyclosporine and tacrolimus may be related to oxidative stress, cellular autophagy, hemodynamics, drug combinations, and long-term drug use [6, 35,36,37]. In terms of oxidative stress and autophagy, both CsA and TAC induce oxidative stress by generating reactive oxygen species (ROS) and impairing mitochondrial function [38, 39]. However, TAC showed stronger performance in disrupting renal tubular autophagy [40, 41]. It has been shown that TAC downregulates autophagy-related proteins (e.g., LC3-II and Beclin- 1) more severely than CsA, leading to accelerated apoptosis in renal tubular cells [41, 42]. In terms of hemodynamic effects, CsA decreases glomerular filtration rate (GFR) by prompting glomerular entry arterioles to constrict mainly through up-regulation of endothelin- 1, in contrast to TAC, which induces endothelial dysfunction and fibrosis and exacerbates mesangial injury through activation of TGF-β1 [43, 44]. Meanwhile, in terms of immune and metabolic pathways, it has been shown that the high affinity of TAC for FK-binding protein 12 (FKBP12) enhances the inhibitory effect of calcineurin, which not only inhibits T-cell activation, but also disrupts the cytoskeletal integrity of the podocytes and promotes the formation of proteinuria. However, CsA promotes inflammation mainly through activation of the JNK/p38 MAPK pathway [45,46,47]. These mechanistic differences are consistent with our finding that TAC has a stronger nephrotoxicity signal (relative risk ratio = 5.17 vs. 4.25 for CsA). Recent studies have further confirmed that the multifaceted toxicity pathways of TAC may explain the higher hospitalization rates due to TAC despite low mortality rates.

Cyclosporine and Tacrolimus are subject to various drug interactions due to their specific metabolic pathways via cytochrome P450, have a narrow therapeutic index, and exhibit clinically relevant dose-dependent and dose-independent toxic effects [48,49,50]. Both CsA and TAC are metabolized by CYP3 A4/5, but genetic variation significantly alters their pharmacokinetics. For example, CYP3 A5-expressors (* 1/1 or 1/* 3 genotypes) have 40–60% lower trough concentrations of TAC than non-expressors (* 3/* 3) and require higher doses to achieve therapeutic goals [51, 52], and in CYP3 A5 non-expressors, increased TAC exposure is associated with a 2.1-fold acute kidney injury risk [53]. It has been shown that individuals harboring the CYP3 A422 allele (rs35599367) display reduced enzyme activity, resulting in a prolonged CsA half-life and a higher risk of nephrotoxicity [54]. These metabolic variants emphasize the importance of therapeutic drug monitoring (TDM) in mitigating nephrotoxicity. Surprisingly, the strongest association between cyclosporine and tacrolimus for kidney injury damage occurring in the real world in this study was toxic nephropathy. It has been shown that the mean trough concentration of tacrolimus is about 7.5 ng/mL in the first 6 months after transplantation, and then decreases gradually, with a mean trough concentration of about 4.5 ng/mL at 5 years; the mean trough concentration of cyclosporine is about 150 ng/mL in the first 6 months after transplantation, and then decreases over time, with a mean trough concentration of about 90 ng/mL at 5 years [55]. It has also been observed in renal transplant recipients that oral clearance of Tac is approximately 2.4-fold higher in CYP3 A5-expressors than in non-expressors, resulting in a 2.5-fold higher dosage requirement in CYP3 A5-expressors, which leads to a significantly higher risk of acute kidney injury [51,52,53, 56, 57]. The above findings all emphasize the importance of TDM combined with pharmacogenetic testing to optimize the safety profile of both drugs. The takeaway is therefore the importance of monitoring drug levels during the use of cyclosporine and tacrolimus. Additionally, we found that tacrolimus demonstrated a higher ROR in most kidney injury-related adverse events, suggesting that tacrolimus may present a higher risk for kidney injury in these instances. Furthermore, the analysis of onset times for adverse reactions revealed that the risk of kidney injury is highest during the initial stages of treatment. This may be attributed to factors such as the initial drug dosage, the patient’s baseline renal function, and individual drug metabolism. Over time, patients may adapt to the medication, or clinicians may adjust the dosage, thereby reducing the risk of kidney injury.

Limitations

Despite the advantages of real-world research and data mining techniques in this study, inevitably, there are some limitations of this study. Firstly, imperfect information such as input errors and incomplete reports during the data mining process may lead to biased analysis, which is caused by the FAERS database itself. Second, only a limited number of reports are identified as duplicates because they may have different CASEIDs but overlapping data. When we tried to remove some duplicates based on event, age, gender, and reporter_country, a large portion of the report was lost, which could be related to missing event_date, age, and gender. Therefore, the method of removing duplicate reports deserves further research. Third, confounding factors are difficult to control. Patients may already have underlying chronic conditions, such as cardiovascular disease, or baseline renal insufficiency and renal complications, which may influence adverse renal responses. Fourth, disproportionate measurements lack incidence denominators, are subject to severe reporting bias, and are not adjusted for confounding, so hypotheses generated by disproportionality analyses need to be further validated by more reliable methods. Despite the aforementioned shortcomings that do exist, the FAERS database was able to identify signals of cyclosporine or tacrolimus and renal injury and further characterize the treatment of cyclosporine or tacrolimus. Our study may provide a new basis for further clinical studies of well-organized cyclosporine or tacrolimus-associated kidney injury.

Data availability

The data used in this study were extracted from the publicly available. FAERS database (https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html).

Abbreviations

FDA:

Food and Drug Administration

FAERS:

FDA Adverse Event Reporting System

GFR:

Glomerular filtration rate

CsA:

Cyclosporine A

TAC:

Tacrolimus

CYP450 3 A4/5:

Cytochrome P450 enzymes

CNI:

Alcineurin inhibitor

ADE:

Adverse drug event

ROR:

Reporting Odds Ratio

PRR:

Proportional Reporting Ratio

MGPS:

Multi-item Gamma-Poisson Shrinkage

BCPNN:

Bayesian Confidence Propagation Neural Network

ADR:

Adverse drug reaction

CI:

Confidence interval

AEs:

Adverse events

References

  1. Wu H, Huang J, Drug-Induced, Nephrotoxicity. Pathogenic mechanisms, biomarkers and prevention strategies. Curr Drug Metab. 2018;19(7):559–67.

    Article  CAS  PubMed  Google Scholar 

  2. Faria J, Ahmed S, Gerritsen KGF, et al. Kidney-based in vitro models for drug-induced toxicity testing. Arch Toxicol. 2019;93(12):3397–418.

    Article  CAS  PubMed  Google Scholar 

  3. Kim SY, Moon A. Drug-induced nephrotoxicity and its biomarkers. Biomol Ther (Seoul). 2012;20(3):268–72.

    Article  CAS  PubMed  Google Scholar 

  4. Mostafavi-Pour Z, Khademi F, Zal F, et al. In vitro analysis of CsA-Induced hepatotoxicity in HepG2 cell line: oxidative stress and Α2 and Β1 integrin subunits expression. Hepat Mon. 2013;13(8):e11447.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Ciresi DL, Lloyd MA, Sandberg SM, et al. The sodium retaining effects of cyclosporine. Kidney Int. 1992;41(6):1599–605.

    Article  CAS  PubMed  Google Scholar 

  6. Wu Q, Wang X, Nepovimova E, et al. Mechanism of cyclosporine A nephrotoxicity: oxidative stress, autophagy, and signalings. Food Chem Toxicol. 2018;118:889–907.

    Article  CAS  PubMed  Google Scholar 

  7. Lai Q, Luo Z, Wu C, et al. Attenuation of cyclosporine A induced nephrotoxicity by schisandrin B through suppression of oxidative stress, apoptosis and autophagy. Int Immunopharmacol. 2017;52:15–23.

    Article  CAS  PubMed  Google Scholar 

  8. Yu M, Liu M, Zhang W, et al. Pharmacokinetics, pharmacodynamics and pharmacogenetics of tacrolimus in kidney transplantation. Curr Drug Metab. 2018;19(6):513–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Alotaibi N. CYP3A5 polymorphisms leading to tacrolimus toxicity following an adult renal transplant. Saudi J Kidney Dis Transpl. 2023;34(3):250–3.

    Article  PubMed  Google Scholar 

  10. Li J, Li H, Li Q, et al. Repaglinide inhibits cyclosporine A-induced renal tubular toxicity by affecting apoptosis and Bax and Bcl-2 expression. Turk J Med Sci. 2018;48(4):880–5.

    Article  CAS  PubMed  Google Scholar 

  11. Nady ME, El-Raouf OMA, El-Sayed EM. Linagliptin ameliorates tacrolimus-induced renal injury: role of Nrf2/HO-1 and HIF-1α/CTGF/PAI-1. Mol Biol Rep. 2024;51(1):608.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Olukman M, Can C, Coşkunsever D, et al. Urotensin receptor antagonist Palosuran attenuates cyclosporine-a-induced nephrotoxicity in rats. Adv Clin Exp Med. 2019;28(10):1393–401.

    Article  PubMed  Google Scholar 

  13. Yilmaz DE, Kirschner K, Demirci H, et al. Immunosuppressive calcineurin inhibitor cyclosporine A induces proapoptotic Endoplasmic reticulum stress in renal tubular cells. J Biol Chem. 2022;298(3):101589.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Yin Y, Shu Y, Zhu J, et al. A real-world pharmacovigilance study of FDA adverse event reporting system (FAERS) events for osimertinib. Sci Rep. 2022;12(1):19555.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. van Puijenbroek EP, Bate A, Leufkens HG, et al. A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions. Pharmacoepidemiol Drug Saf. 2002;11(1):3–10.

    Article  PubMed  Google Scholar 

  16. Aliabadi T, Saberi EA, Motameni Tabatabaei A, et al. Antibiotic use in endodontic treatment during pregnancy: A narrative review. Eur J Transl Myol. 2022;32(4):10813.

  17. Trippe ZA, Brendani B, Meier C, et al. Identification of substandard medicines via disproportionality analysis of individual case safety reports. Drug Saf. 2017;40(4):293–303.

    Article  CAS  PubMed  Google Scholar 

  18. Åberg F, Sallinen V, Tuominen S, et al. Cyclosporine vs. tacrolimus after liver transplantation for primary sclerosing cholangitis - a propensity score-matched intention-to-treat analysis. J Hepatol. 2024;80(1):99–108.

    Article  PubMed  Google Scholar 

  19. Krämer BK, Montagnino G, Del Castillo D, et al. Efficacy and safety of tacrolimus compared with cyclosporin A microemulsion in renal transplantation: 2 year follow-up results. Nephrol Dial Transpl. 2005;20(5):968–73.

    Article  Google Scholar 

  20. Lemaitre F, Budde K, Van Gelder T, et al. Therapeutic drug monitoring and dosage adjustments of immunosuppressive drugs when combined with Nirmatrelvir/Ritonavir in patients with COVID-19. Ther Drug Monit. 2023;45(2):191–9.

    Article  CAS  PubMed  Google Scholar 

  21. Brinkert F, Kemper MJ, Briem-Richter A, et al. High prevalence of renal dysfunction in children after liver transplantation: non-invasive diagnosis using a Cystatin C-based equation. Nephrol Dial Transpl. 2011;26(4):1407–12.

    Article  CAS  Google Scholar 

  22. Li X, Zhu W, Bao J, et al. Efficacy and safety of cyclosporine-based regimens for primary immune thrombocytopenia: a systematic review and meta-analysis. J Int Med Res. 2023;51(1):3000605221149870.

    Article  PubMed  Google Scholar 

  23. Camacho EM, Verstappen SM, Lunt M, et al. Influence of age and sex on functional outcome over time in a cohort of patients with recent-onset inflammatory polyarthritis: results from the Norfolk arthritis register. Arthritis Care Res (Hoboken). 2011;63(12):1745–52.

    Article  PubMed  Google Scholar 

  24. Smolen JS, Aletaha D, McInnes IB. Rheumatoid arthritis. Lancet. 2016;388(10055):2023–38.

    Article  CAS  PubMed  Google Scholar 

  25. Wolfe F, Michaud K. Anemia and renal function in patients with rheumatoid arthritis. J Rheumatol. 2006;33(8):1516–22.

    PubMed  Google Scholar 

  26. Bentata Y, Tacrolimus. 20 Years of use in adult kidney transplantation. What we should know about its nephrotoxicity. Artif Organs. 2020;44(2):140–52.

    Article  PubMed  Google Scholar 

  27. Ito A, Okada Y, Hashita T, et al. Sex differences in the blood concentration of tacrolimus in systemic lupus erythematosus and rheumatoid arthritis patients with CYP3A5*3/*3. Biochem Genet. 2017;55(3):268–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Shi Y, Li Y, Tang J, et al. Influence of CYP3A4, CYP3A5 and MDR-1 polymorphisms on tacrolimus pharmacokinetics and early renal dysfunction in liver transplant recipients. Gene. 2013;512(2):226–31.

    Article  CAS  PubMed  Google Scholar 

  29. Rojas L, Neumann I, Herrero MJ, et al. Effect of CYP3A5*3 on kidney transplant recipients treated with tacrolimus: a systematic review and meta-analysis of observational studies. Pharmacogenomics J. 2015;15(1):38–48.

    Article  CAS  PubMed  Google Scholar 

  30. Lee FY, Islahudin F, Ali Nasiruddin AY, et al. Effects of CYP3A5 polymorphism on rapid progression of chronic kidney disease: A prospective, multicentre study. J Pers Med. 2021;11(4):252.

  31. Vincenti F, Jensik SC, Filo RS, et al. A long-term comparison of tacrolimus (FK506) and cyclosporine in kidney transplantation: evidence for improved allograft survival at five years. Transplantation. 2002;73(5):775–82.

    Article  CAS  PubMed  Google Scholar 

  32. Moreno JM, Cuervas-Mons V, Rubio E, et al. Chronic renal dysfunction after liver transplantation in adult patients: prevalence, risk factors, and impact on mortality. Transplant Proc. 2003;35(5):1907–8.

    Article  CAS  PubMed  Google Scholar 

  33. Suilik HA, Al-Shammari AS, Soliman Y, et al. Efficacy of tacrolimus versus cyclosporine after lung transplantation: an updated systematic review, meta-analysis, and trial sequential analysis of randomized controlled trials. Eur J Clin Pharmacol. 2024;80(12):1923–35.

    Article  CAS  PubMed  Google Scholar 

  34. Kamel M, Kadian M, Srinivas T, et al. Tacrolimus confers lower acute rejection rates and better renal allograft survival compared to cyclosporine. World J Transpl. 2016;6(4):697–702.

    Article  Google Scholar 

  35. Demirci H, Popovic S, Dittmayer C, et al. Immunosuppression with cyclosporine versus tacrolimus shows distinctive nephrotoxicity profiles within renal compartments. Acta Physiol (Oxf). 2024;240(8):e14190.

    Article  CAS  PubMed  Google Scholar 

  36. Nishida S, Ishima T, Kimura N, et al. Metabolomic profiling of mice with Tacrolimus-Induced nephrotoxicity: carnitine deficiency in renal tissue. Biomedicines. 2024;12(3):521.

  37. Yu J, Wei X, Gao J, et al. Role of cyclosporin A in the treatment of kidney disease and nephrotoxicity. Toxicology. 2023;492:153544.

    Article  CAS  PubMed  Google Scholar 

  38. Khanna AK, Pieper GM. NADPH oxidase subunits (NOX-1, p22phox, Rac-1) and tacrolimus-induced nephrotoxicity in a rat renal transplant model. Nephrol Dial Transpl. 2007;22(2):376–85.

    Article  CAS  Google Scholar 

  39. Ara C, Dirican A, Unal B, et al. The effect of melatonin against FK506-induced renal oxidative stress in rats. Surg Innov. 2011;18(1):34–8.

    Article  PubMed  Google Scholar 

  40. Park C, Kwon DH, Hwang SJ, et al. Protective effects of Nargenicin A1 against Tacrolimus-Induced oxidative stress in Hirame natural embryo cells. Int J Environ Res Public Health. 2019;16(6):1044.

  41. Hisamura F, Kojima-Yuasa A, Kennedy DO, et al. Protective effect of green tea extract and tea polyphenols against FK506-induced cytotoxicity in renal cells. Basic Clin Pharmacol Toxicol. 2006;98(2):192–6.

    Article  CAS  PubMed  Google Scholar 

  42. Dagar N, Kale A, Steiger S, et al. Receptor-mediated mitophagy: an emerging therapeutic target in acute kidney injury. Mitochondrion. 2022;66:82–91.

    Article  CAS  PubMed  Google Scholar 

  43. Fassi A, Sangalli F, Colombi F, et al. Beneficial effects of calcium channel Blockade on acute glomerular hemodynamic changes induced by cyclosporine. Am J Kidney Dis. 1999;33(2):267–75.

    Article  CAS  PubMed  Google Scholar 

  44. Li X, Zhuang S. Recent advances in renal interstitial fibrosis and tubular atrophy after kidney transplantation. Fibrogenesis Tissue Repair. 2014;7:15.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Blankenstein KI, Borschewski A, Labes R, et al. Calcineurin inhibitor cyclosporine A activates renal Na-K-Cl cotransporters via local and systemic mechanisms. Am J Physiol Ren Physiol. 2017;312(3):F489–501.

    Article  CAS  Google Scholar 

  46. Hoorn EJ, Walsh SB, McCormick JA, et al. The calcineurin inhibitor tacrolimus activates the renal sodium chloride cotransporter to cause hypertension. Nat Med. 2011;17(10):1304–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. He B, Li QY, Wu YY, et al. Cyclosporin A protects JEG-3 cells against oxidative stress-induced apoptosis by inhibiting the p53 and JNK/p38 signaling pathways. Reprod Biol Endocrinol. 2020;18(1):100.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Brunet M, van Gelder T, Åsberg A, et al. Therapeutic drug monitoring of Tacrolimus-Personalized therapy: second consensus report. Ther Drug Monit. 2019;41(3):261–307.

    Article  CAS  PubMed  Google Scholar 

  49. Ong SC, Gaston RS. Thirty years of tacrolimus in clinical practice. Transplantation. 2021;105(3):484–95.

    Article  CAS  PubMed  Google Scholar 

  50. Wu Q, Kuca K. Metabolic pathway of cyclosporine A and its correlation with nephrotoxicity. Curr Drug Metab. 2019;20(2):84–90.

    Article  CAS  PubMed  Google Scholar 

  51. Li DY, Teng RC, Zhu HJ, et al. CYP3A4/5 polymorphisms affect the blood level of cyclosporine and tacrolimus in Chinese renal transplant recipients. Int J Clin Pharmacol Ther. 2013;51(6):466–74.

    Article  CAS  PubMed  Google Scholar 

  52. Huang L, Wang J, Yang J, et al. Impact of CYP3A4/5 and ABCB1 polymorphisms on tacrolimus exposure and response in pediatric primary nephrotic syndrome. Pharmacogenomics. 2019;20(15):1071–83.

    Article  CAS  PubMed  Google Scholar 

  53. Nuchjumroon A, Vadcharavivad S, Singhan W, et al. Comparison of tacrolimus Intra-Patient variability during 6–12 months after kidney transplantation between CYP3A5 expressers and nonexpressers. J Clin Med. 2022;11:21.

    Article  Google Scholar 

  54. Elens L, Bouamar R, Hesselink DA, et al. The new CYP3A4 intron 6 C > T polymorphism (CYP3A4*22) is associated with an increased risk of delayed graft function and worse renal function in cyclosporine-treated kidney transplant patients. Pharmacogenet Genomics. 2012;22(5):373–80.

    Article  CAS  PubMed  Google Scholar 

  55. Ciancio G, Gaynor JJ, Guerra G, et al. Long-term effects of average calcineurin inhibitor trough levels (over time) on renal function in a prospectively followed cohort of 150 kidney transplant recipients. Clin Transl Sci. 2023;16(11):2382–93.

    Article  PubMed  PubMed Central  Google Scholar 

  56. de Jonge H, de Loor H, Verbeke K, et al. In vivo CYP3A4 activity, CYP3A5 genotype, and hematocrit predict tacrolimus dose requirements and clearance in renal transplant patients. Clin Pharmacol Ther. 2012;92(3):366–75.

    Article  PubMed  Google Scholar 

  57. Laskow DA, Vincenti F, Neylan JF, et al. An open-label, concentration-ranging trial of FK506 in primary kidney transplantation: a report of the united States multicenter FK506 kidney transplant group. Transplantation. 1996;62(7):900–5.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Thanks to the US Food and Drug Administration (FDA) for providing access to the FAERS database for this study.

Declaration of interests

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Funding

No funding is available for this article.

Author information

Author notes

  1. Min Xu and Shanggang Xu contributed equally to this work.

Authors and Affiliations

  1. Department of Respiratory, Clinical Medical College & Affliated Hospital of Chengdu University, Chengdu University, Chengdu, 610036, China

    Min Xu

  2. Department of Emergency, Clinical Medical College & Affliated Hospital of Chengdu University, Chengdu University, Chengdu, 610036, China

    Shanggang Xu & Xueliang Yi

Authors
  1. Min Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Shanggang Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Xueliang Yi
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Min Xu: Writing–original draft and editing. Shanggang Xu: Conceptualization, Writing–review. Xueliang Yi: Formal Analysis, Visualization, Writing–original draft, Writing–review and editing.

Corresponding author

Correspondence to Xueliang Yi.

Ethics declarations

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, M., Xu, S. & Yi, X. A comparative analysis of drug-induced kidney injury adverse reactions between cyclosporine and tacrolimus based on the FAERS database. BMC Immunol 26, 35 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12865-025-00714-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12865-025-00714-7

Keywords

BMC Immunology

ISSN: 1471-2172

Contact us